| Literature DB >> 27346902 |
Junbo Duan1, Charles Soussen2, David Brie2, Jérôme Idier3, Mingxi Wan1, Yu-Ping Wang4.
Abstract
This paper studies the intrinsic connection between a generalized LASSO and a basic LASSO formulation. The former is the extended version of the latter by introducing a regularization matrix to the coefficients. We show that when the regularization matrix is even- or under-determined with full rank conditions, the generalized LASSO can be transformed into the LASSO form via the Lagrangian framework. In addition, we show that some published results of LASSO can be extended to the generalized LASSO, and some variants of LASSO, e.g., robust LASSO, can be rewritten into the generalized LASSO form and hence can be transformed into basic LASSO. Based on this connection, many existing results concerning LASSO, e.g., efficient LASSO solvers, can be used for generalized LASSO.Entities:
Keywords: LASSO; deconvolution; diagonally dominant; generalized LASSO; robust LASSO; solution path; total variation
Year: 2016 PMID: 27346902 PMCID: PMC4917299 DOI: 10.1016/j.sigpro.2016.03.001
Source DB: PubMed Journal: Signal Processing ISSN: 0165-1684 Impact factor: 4.662